Method and apparatus for multi-spectral imaging and analysis of skin lesions and biological tissues

• US 9,572,494 B2
• Filed: 08/11/2009
• Issued: 02/21/2017
• Est. Priority Date: 08/12/2008
• Status: Active Grant

• Current Assignee: New Jersey Institute of Technology
• Original Assignee: New Jersy Institute of Technology
• Inventors: Dhawan, Atam Prakash
• Primary Examiner(s): Jung, Unsu
• Assistant Examiner(s): FISHER, MICHAEL NAPOLEON
• Application Number:
  US12/539,049
• Publication Number:
  US 20100042004A1
• Time in Patent Office: 2,751 Days
• Field of Search: 600/301, 600/306, 600/310, 600/320, 600/323, 600/364, 600/416, 600/419, 600/472, 600/473, 600/476, 356/3.11, 356/3.14, 356/4.01, 356/4.03, 356/5.14, 356/40, 356/41, 382/128
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/443 Evaluating skin constituents, e.g. elastin, melanin, water
• CPC Class Codes: A61B 5/444 Evaluating skin marks, e.g. mole, nevi, tumour, scar
• CPC Class Codes: A61B 5/445 Evaluating skin irritation or skin trauma, e.g. rash, eczema, wound, bed sore
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A multispectral nevoscope that uses specific wavelengths in the visible and infrared spectrum of electromagnetic radiation to transilluminate a skin-lesion or a biological tissue or specimen for imaging and maps multispectral 2-dimensional images into 3-dimensional virtual space for providing 3-D distributions of pre-defined parameters representing the characteristic properties (such as melanin, hemoglobin and deoxyhemoglobin, etc.) of a skin lesion. Methods are disclosed for analyzing and using the characteristic distributions of specific parameters for detection and management of skin-cancers, or characterization of a biological tissue or specimen.

Claims

1. A method of reconstructing skin lesion images comprising transilluminating a skin lesion using a nevoscope by applying visible light and near-infrared wavelengths in the range of 400 nm to 1000 nm in a converging ring to the skin lesion, dividing a detector space of the nevoscope into a plurality of concentric rings with equal width, wherein an innermost ring contains a plurality of detectors with equal size and remaining rings are split into a number of detectors which maintain the same area as the detectors in the innermost ring, collecting information about absorption and scattering properties of melanin, oxyhemoglobin and/or deoxyhemoglobin of skin layers and the skin lesion, the collecting step comprising obtaining reflectance-based absorption measurements with different levels of depth of penetration to estimate volumes of melanin, oxyhemoglobin and deoxyhemoglobin, retrieving information regarding the distribution of melanin, oxyhemoglobin and/or deoxyhemoglobin in the skin layers and lesion, applying a shape-based multi-constrained algorithm to the collected and retrieved information, and using results obtained from applying the algorithm to reconstruct a skin lesion image based on shapes of the melanin, oxyhemoglobin and/or deoxyhemoglobin wherein the melanin, oxyhemoglobin and/or deoxyhemoglobin are delineated by a first and a second cubic tensor-product B-spline surface, wherein the multi-constrained algorithm comprises a linearized forward model evaluated by Monte Carlo simulation in terms of typical optical properties of normal skin, generating a Jacobian matrix, applying a genetic algorithm to generate predicted shape, performing a sampling function, evaluating the Jacobian matrix by recording trajectories for one detector of a given ring and generating trajectories of remaining detectors by rotating recorded trajectories, and applying the Jacobian matrix to the result of the sampling function to generate a best possible reconstruction solution.

2. The method according to claim 1 wherein malignant melanoma is represented by shape parameters of a melanin part and one or more of an oxyhemoglobin or deoxyhemoglobin part and these parameters are grouped into genetic algorithms.

3. The method according to claim 1 wherein the wavelengths are in the range of 500 nm to 960 nm.

4. The method according to claim 1 wherein the first cubic tensor-product B-spline surface is represented as function ƒ

₁(x,y) which corresponds to a depth of the skin lesion from an epidermal layer at a position (x,y), positioning an N×

N rectangular grid to lie over the epidermal layer, sampling the function ƒ

₁(x,y) to N×

N discrete values ƒ

_d1(X,Y) wherein (x,y) is continuous and (X,Y) is N×

N numbers of discrete sampling positions, interpolating the discrete values by the cubic tensor-product B-spline which satisfies the condition

5. The method according to claim 4 wherein the parameters to determine the melanin shape are N×

N discrete values ƒ

_d1(X,Y).

6. The method according to claim 4 wherein the parameters to determine the melanin shape are the corresponding B-spline coefficients c₁(i,j).

7. The method according to claim 4 wherein the second cubic tensor-product B-spline surface is defined by N×

N discrete values ƒ

_d2(X,Y).

8. The method according to claim 4 wherein the second cubic tensor-product B-spline surface is defined by z_d(X,Y)=ƒ

_d2(X,Y)−

ƒ

f_d1(X,Y) which is the thickness of blood region between the first surface and the second surface.

9. A method of reconstructing skin lesion images comprising transilluminating a skin lesion using a nevoscope by applying visible light and near-infrared wavelengths in the range of 400 nm to 1000 nm in a converging ring to the skin lesion, collecting information about absorption and scattering properties of melanin, oxyhemoglobin and/or deoxyhemoglobin of skin layers and the skin lesion, the collecting step comprising obtaining reflectance-based absorption measurements with different levels of depth of penetration to estimate volumes of melanin, oxyhemoglobin and deoxyhemoglobin, retrieving information regarding the distribution of melanin, oxyhemoglobin and/or deoxyhemoglobin in the skin layers and lesion, applying a shape-based multi-constrained algorithm to the collected and retrieved information, and using results obtained from applying the algorithm to reconstruct a skin lesion image based on shapes of the melanin, oxyhemoglobin and/or deoxyhemoglobin wherein the melanin, oxyhemoglobin and/or deoxyhemoglobin are delineated by a first and a second cubic tensor-product B-spline surface, and wherein the first cubic tensor-product B-spline surface is represented as function ƒ

₁(x,y) which corresponds to a depth of the skin lesion from an epidermal layer at a position (x,y), positioning an N×

N rectangular grid to lie over the epidermal layer, sampling the function ƒ

₁(x,y) to N×

N discrete values ƒ

_d1(X,Y) wherein (x,y) is continuous and (X,Y) is N×

N numbers of discrete sampling positions, interpolating the discrete values by the cubic tensor-product B-spline which satisfies the condition

10. The method according to claim 9 wherein the parameters to determine the melanin shape are N×

N discrete values ƒ

_d1(X,Y).

11. The method according to claim 9 wherein the parameters to determine the melanin shape are the corresponding B-spline coefficients c₁(i,j).

12. The method according to claim 9 wherein the second cubic tensor-product B-spline surface is defined by N×

N discrete values ƒ

_d2(X,Y).

13. The method according to claim 9 wherein the second cubic tensor-product B-spline surface is defined by z_d(X,Y)=ƒ

_d2(X,Y)−

ƒ

_d1(X,Y) which is the thickness of blood region between the first surface and the second surface.

14. The method according to claim 9 wherein the multi-constrained algorithm comprises a linearized forward model evaluated by Monte Carlo simulation in terms of typical optical properties of normal skin, generating a Jacobian matrix, applying a genetic algorithm to generate predicted shape, performing a sampling function, and applying the Jacobian matrix to the result of the sampling function to generate a best possible reconstruction solution.

15. The method according to claim 9 wherein malignant melanoma is represented by shape parameters of a melanin part and one or more of an oxyhemoglobin or deoxyhemoglobin part and these parameters are grouped into genetic algorithms.

16. The method according to claim 9 wherein the wavelengths are in the range of 500 nm to 960 nm.

17. The method according to claim 9 comprising dividing a detector space of the nevoscope into a plurality of concentric rings with equal width, wherein an innermost ring contains a plurality of detectors with equal size and remaining rings are split into a number of detectors which maintain the same area as the detectors in the innermost ring, and evaluating a Jacobian matrix by recording trajectories for one detector of a given ring and generating trajectories of remaining detectors by rotating recorded trajectories.